Multimodal Surface Matching with Higher-Order Smoothness Constraints<sup>☆</sup>

Garcia, Kara; Glasser, Matthew F.; Chen, Zhengdao; Coalson, Timothy S.; Makropoulos, Antonios; Božek, Jelena; Wright, Robert; Schuh, Andreas; Webster, Matthew; Hutter, Jana; Price, Anthony N.; Cordero‐Grande, Lucilio; Hughes, Emer; Tusor, Nora; Bayly, Philip V.; Essen, David C. Van; Edwards, Anthony; Smith, Stephen M.; Hajnal, Joseph V.; Jenkinson, Mark; Glocker, Ben; Rueckert, Daniel

doi:10.1101/178962

Cited by 52 publications

(98 citation statements)

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“…White and pial cortical surfaces were 19 reconstructed from the structural scans using the HCP Pipelines (Glasser et al, 2013). Surfaces were 20 aligned across subjects to the HCP 32k fs_LR standard surface space using first a gentle folding-based 21 registration 'MSMSulc' and then a more aggressive areal-feature-based registration 'MSMAll' that was 22 driven by myelin maps, resting-state network maps, and 3T resting-state visuotopic maps (Robinson et 23 al., 2014;Glasser et al, 2016;Robinson et al, 2018). Myelin maps were based on the ratio of T1w/T2w 24 images (Glasser and Van Essen, 2011), normalized using a surface-based atlas to estimate B1+ transmit 25 effects (Glasser et al, 2013).…”

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confidence: 99%

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The HCP 7T Retinotopy Dataset: Description and pRF Analysis

Benson

Arcaro

et al. 2018

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About a quarter of human cerebral cortex is tiled with maps of the visual field. These maps can be 3 measured with functional magnetic resonance imaging (fMRI) while subjects view spatially modulated 4 visual stimuli, also known as 'retinotopic mapping'. One of the datasets collected by the Human 5Connectome Project (HCP) involved ultra-high-field (7 Tesla) fMRI retinotopic mapping in 181 healthy 6 adults (1.6-mm resolution), yielding the largest freely available collection of retinotopy data. Here, we 7 describe the experimental paradigm and the results of model-based analysis of the fMRI data. These 8 results provide estimates of population receptive field position and size. Our analyses include both results 9 from individual subjects as well as results obtained by averaging fMRI time-series across subjects at each 10 cortical and subcortical location and then fitting models. Both the group-average and individual-subject 11 results reveal robust signals across much of the brain, including occipital, temporal, parietal, and frontal 12 cortex as well as subcortical areas. The group-average results agree well with previously published 13 parcellations of visual areas. In addition, split-half analyses demonstrate strong within-subject reliability, 14 further evidencing the high quality of the data. We make publicly available the analysis results for 15 individual subjects and the group average, as well as associated stimuli and analysis code. These 16 resources provide an opportunity for studying fine-scale individual variability in cortical and subcortical 17 organization and the properties of high-resolution fMRI. In addition, they provide a measure that can be 18 combined with other HCP measures acquired in these same participants. This enables comparisons 19 across groups, health, and age, and comparison of organization derived from a retinotopic task against 20 that derived from other measurements such as diffusion imaging and resting-state functional connectivity. 21 22. CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/308247 doi: bioRxiv preprint first posted online Apr. 25, 2018; 3 Introduction 1 2The central nervous system maps sensory inputs onto topographically organized representations. In the 3 field of vision, researchers have successfully exploited functional magnetic resonance imaging (fMRI) to 4 noninvasively measure visual field representations ('retinotopy') in the living human brain (Engel et al., 5 1994;Sereno et al., 1995; DeYoe et al., 1996; Engel et al., 1997). These efforts enable parcellation of 6 visual cortex into distinct maps of the visual field, thereby laying the foundation for detailed investigations 7 of the properties of visual cortex (parcellation references: Abdollahi et al., 2014;Benson et al., 2014; 8 Wang et al., 2015; review references: Tootell et al., 1996;Wandell et...

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confidence: 99%

“…The pre-processed time-series data (CIFTI format) reflect MSMAll-alignment of individual subjects to the 15 fs_LR surface (Glasser et al, 2013;Robinson et al, 2018). The pRF model solutions are obtained by 16 fitting each CIFTI grayordinate independently; thus, there are no additional spatial transformations 17 applied.…”

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confidence: 99%

The HCP 7T Retinotopy Dataset: Description and pRF Analysis

Benson

Arcaro

et al. 2018

Preprint

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“…Significant advances have already been made in recent years in order to tackle the issue of spatial misalignment across individuals. For example, the HCP data used in this work were spatially aligned using the multimodal surface mapping (MSM) technique, which achieves very good functional alignment by using features that are more closely tied to cortical areas (although note that, since the time of the HCP release, refinements to the [regularisation of the] MSM algorithm have resulted in further improvements in the observed functional alignment of HCP data (Robinson et al, 2014(Robinson et al, , 2017 ). Therefore, gross misalignment is unlikely to play a role in our results.…”

Section: Discussionmentioning

confidence: 99%

“…The presence of this behaviourally meaningful spatial variability is somewhat surprising, because these data were aligned using a Multimodal Surface Matching (MSM) approach (Robinson et al, 2014(Robinson et al, , 2017 , driven by both structural and functional cortical features (including myelin maps and resting state network maps). MSM has been shown to achieve very good functional alignment compared with other methods, and particularly compared with volumetric alignment approaches or surface-based approaches that use cortical folding patterns rather than areal features (Coalson, Van Essen, & Glasser, n.d.) .…”

Section: Figurementioning

confidence: 99%

“…HCP data are freely available from https://db.humanconnectome.org . The version of MSMAll that is compatible with the approach implemented for the alignment of HCP data can be found here: http://www.doc.ic.ac.uk/~ecr05/MSM_HOCR_v2/ (Robinson et al, 2017) . Matlab code used in this work can be found here: https://github.com/JanineBijsterbosch/Spatial_netmat .…”

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The relationship between spatial configuration and functional connectivity of brain regions

Bijsterbosch

Woolrich

Glasser

et al. 2017

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Brain connectivity is often considered in terms of the communication between functionally distinct brain regions. Many studies have investigated the extent to which patterns of coupling strength between multiple neural populations relates to behavior. For example, studies have used "functional connectivity fingerprints" to characterise individuals' brain activity. Here, we investigate the extent to which the exact spatial arrangement of cortical regions interacts with measures of brain connectivity. We find that the shape and exact location of brain regions interact strongly with the modelling of brain connectivity, and present evidence that the spatial arrangement of functional regions is strongly predictive of non-imaging measures of behaviour and lifestyle. We believe that, in many cases, cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity. Therefore, a better understanding of these effects is important when interpreting the relationship between functional imaging data and cognitive traits.

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Time‐varying measures of cerebral network centrality correlate with visual saliency during movie watching

Ogawa

2021

Brain and Behavior

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The extensive development of graph‐theoretic analysis for functional connectivity has revealed the multifaceted characteristics of brain networks. Network centralities identify the principal functional regions, individual differences, and hub structure in brain networks. Neuroimaging studies using movie‐watching have investigated brain function under naturalistic stimuli. Visual saliency is one of the promising measures for revealing cognition and emotions driven by naturalistic stimuli. This study investigated whether the visual saliency in movies was associated with network centrality. The study examined eigenvector centrality (EC), which is a measure of a region's influence in the brain network, and the participation coefficient (PC), which reflects the hub structure in the brain, was used for comparison. Static and time‐varying EC and PC were analyzed by a parcel‐based technique. While EC was correlated with brain activity in parcels in the visual and auditory areas during movie‐watching, it was only correlated with parcels in the visual areas in the retinotopy task. In addition, high PC was consistently observed in parcels in the putative hub both during the tasks and the resting‐state condition. Time‐varying EC in the parietal parcels and time‐varying PC in the primary sensory parcels significantly correlated with visual saliency in the movies. These results suggest that time‐varying centralities in brain networks are distinctively associated with perceptual processing and subsequent higher processing of visual saliency.

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Multimodal Surface Matching with Higher-Order Smoothness Constraints^☆

Abstract: In brain imaging, accurate alignment of cortical surfaces is fundamental to the statistical sensitivity and spatial localisation of group studies; and cortical surface-based alignment has generally been accepted to be superior

Cited by 52 publications

References 71 publications

The HCP 7T Retinotopy Dataset: Description and pRF Analysis

The HCP 7T Retinotopy Dataset: Description and pRF Analysis

The relationship between spatial configuration and functional connectivity of brain regions

Time‐varying measures of cerebral network centrality correlate with visual saliency during movie watching

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Multimodal Surface Matching with Higher-Order Smoothness Constraints☆

Abstract: In brain imaging, accurate alignment of cortical surfaces is fundamental to the statistical sensitivity and spatial localisation of group studies; and cortical surface-based alignment has generally been accepted to be superior

Cited by 52 publications

References 71 publications

The HCP 7T Retinotopy Dataset: Description and pRF Analysis

The HCP 7T Retinotopy Dataset: Description and pRF Analysis

The relationship between spatial configuration and functional connectivity of brain regions

Time‐varying measures of cerebral network centrality correlate with visual saliency during movie watching

Contact Info

Product

Resources

About

Multimodal Surface Matching with Higher-Order Smoothness Constraints^☆